The detection of objects using infrared sensors has numerous practical applications. For example, infrared sensors can be used in astronomy to acquire and track objects such as stars, asteroids, and comets. Similarly, infrared sensors can be used to track and control commercial and military aircraft and missiles. Moreover, infrared sensing can be used in the medical field to image small moving objects, such as cells.
Precise estimates of an infrared objects' radiant intensity (amplitude) and the objects' direction (azimuth and elevation angles) are critical to acquiring and tracking these objects.
Although current infrared systems acquire and track infrared objects satisfactorily, room for improvement exists. Specifically, current infrared sensing systems are required to fit three parameters (e.g., amplitude, azimuth, and elevation angles). These iterative curve fitting process are computationally intensive and time consuming to perform. Moreover, current amplitude and position estimation algorithms (e.g., Newton algorithms), which differentially update their initial amplitude and position parameter estimates, can exhibit oscillations in their estimates which can cause false local minimums in their residual functions.
Accordingly, there is a need for improved methods and systems for determining infrared objects' amplitude and position.